1. Field of the Invention
The present invention relates to methods for attentive clustering of online authors, targets of attention, and related analytics.
2. Description of the Related Art
Internet-based technologies, and the manifold genres of interaction they afford, are re-architecting public and private communications alike and thus altering the relationships between all manner of social actors, from individuals, to organizations, to mass media institutions. Internet technologies have enabled shifts in methods and practices of interpersonal communication. Many-to-many and social scale-spanning Internet communications technologies are eliminating the channel-segregation that previously reinforced the independence of classes of actors at these levels of scale, enabling (or more accurately in many cases, forcing) them to represent themselves to one another via a common medium, and increasingly in ways that are universally visible, searchable and persistent.
Online readers typically navigate hyperlinked chains of related stories, bouncing between numerous websites in a hypertext network, returning periodically to favored starting points to pick up new trails. Hyperlinks result from a combination of choices, from those made by individual, autonomous authors to those made programmatically by designed systems, such as permalinks, site navigation, embedded advertising, tracking services, and the like. Human authors practice the same kind of information selectivity online that they do offline, i.e. what authors (including those representing organizations) write about and link to reflects somewhat stable interests, attitudes, and social/organizational relationships. The structure of the network formed by these hyperlinks is a product of these choices, and thus large-scale regularities in choices will be evident in macro-level structure. This structure will thus bear the mark of individual preferences and characteristics of designed systems and suggests a kind of “flow map” of how the Internet channels attention to online resources. Discriminating among types of links, and the ability to select categories of those which represent author choices, allows structural analytics to discover similarities among authors. Errors, randomness, or noise in linking at the individual level has local, independent causes, and does not bias large-scale macro patterns.
Thus, in order to understand and leverage the online information ecosystem, there remains a need for systems and methods for structural analytics aimed at identifying clusters of online readers and influential authors, discovering how they drive traffic to particular online resources, and leveraging that knowledge across various applications ranging from targeted advertising and communication to expert identification, and the like.